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Title: An enhanced information sharing roadside unit allocation scheme for vehicular networks
Authors: Magsino, ER 
Ho, IWH 
Issue Date: 2022
Source: IEEE transactions on intelligent transportation systems, 2022, Early Access, p. 1-14,
Abstract: Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA–RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA–RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area’s average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA–RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.
Keywords: Roadside unit allocation
Information sharing
Vehicular networks
Spatiotemporal coverage
Efficiency and effectiveness
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on intelligent transportation systems 
ISSN: 1524-9050
EISSN: 1558-0016
DOI: 10.1109/TITS.2022.3140801
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